Data Analyst & BI Dashboard Developer · Excel · Power BI · SQL · Python
I help operations-heavy businesses — garments/manufacturing, finance and NGOs — turn messy, scattered data into clean datasets, automated reports and decision-ready dashboards. This repository contains four end-to-end projects, each with the raw data, the cleaning steps, the analysis, and a written case study with business recommendations.
| # | Project | Tools | What it demonstrates |
|---|---|---|---|
| 01 | Retail Sales & Profitability Dashboard | Excel, Power Query, PivotTables | Cleaning, KPI cards, an interactive dashboard, and discount-vs-profit analysis |
| 02 | Financial Transaction Analysis | PostgreSQL, SQL | Validating 100,000 records, a cleaned view, cash-flow classification and financial KPIs |
| 03 | Marketing Campaign Analysis | Python, Pandas, Matplotlib | EDA on 200,000 records, ROI/CTR/cost-per-conversion, and budget recommendations |
| 04 | HR Attrition & Workforce Dashboard | Power BI, DAX, Power Query | Attrition, retention and satisfaction analysis in an interactive Power BI report |
An executive Excel dashboard on Superstore retail data (Kaggle). Raw data was cleaned in Power Query, then analysed to show which regions, categories and customer segments drive sales and profit — and where discounting quietly erodes margin.
A PostgreSQL analysis of 100,000 financial transactions (Kaggle). Includes data-quality checks, a cleaned SQL view, and classification into inflows, outflows and transfers, surfacing KPIs such as total transaction value and estimated net cash flow. A data limitation (identical debit/credit values) is documented and handled transparently.
➡️ Read the case study · Business queries
An exploratory analysis of 200,000 marketing-campaign records (Kaggle) using Pandas and Matplotlib — comparing channels, campaign types and customer segments on ROI, conversion and cost efficiency to guide budget allocation.
➡️ Read the case study · Notebook
An interactive Power BI report on HR data covering attrition, retention, satisfaction and department-level segmentation, built with Power Query cleaning and DAX measures.
➡️ Dashboard plan · DAX measures
I pair data skills with hands-on business-operations experience — garments/manufacturing, finance and NGO reporting — so I start from the business question, not just the chart. I hold the Google Data Analytics Professional Certificate (2023) and work day-to-day with Excel, Power BI, SQL (PostgreSQL/MySQL) and Python.
Datasets used here are public (Kaggle) and chosen to demonstrate method and reasoning; client work is handled confidentially.